Recommender systems are widely used for information filtering, especially for various online services such as E-commerce [1]}, multimedia platforms [2]}, [3]} and social media [4]}. Among existing systems, training using implicit feedback (e.g. view and click behaviours) is more prevalent than that using explicit ratings, since implicit feedback is much easier to be collected [5]}, [6]}.
Specifically, the
interacted user-item samples
are assumed to be positive examples while negative examples are sampled from
missing
interactions. Under this assumption, the interacted examples are regarded to denote positive user preference (i.e., like) and thus are trained towards higher prediction scores. The sampled missing interactions are considered as negative user preferences (i.e., dislike) and are pushed down in the training procedure. Figure REF  illustrates this normal training assumption.
